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def nms(boxes, scores, nms_thr): """Single class NMS implemented in Numpy.""" x1 = boxes[:, 0] y1 = boxes[:, 1] x2 = boxes[:, 2] y2 = boxes[:, 3] areas = (x2 - x1 + 1) * (y2 - y1 + 1) order = scores.argsort()[::-1] keep = [] while order.size > 0: i = order[0] keep.append(i) xx1 = np.maximum(x1[i], x1[order[1:]]) yy1 = np.maximum(y1[i], y1[order[1:]]) xx2 = np.minimum(x2[i], x2[order[1:]]) yy2 = np.minimum(y2[i], y2[order[1:]]) w = np.maximum(0.0, xx2 - xx1 + 1) h = np.maximum(0.0, yy2 - yy1 + 1) inter = w * h ovr = inter / (areas[i] + areas[order[1:]] - inter) inds = np.where(ovr <= nms_thr)[0] order = order[inds + 1] return keep def multiclass_nms(boxes, scores, nms_thr, score_thr): """Multiclass NMS implemented in Numpy""" final_dets = [] num_classes = scores.shape[1] for cls_ind in range(num_classes): cls_scores = scores[:, cls_ind] valid_score_mask = cls_scores > score_thr if valid_score_mask.sum() == 0: continue else: valid_scores = cls_scores[valid_score_mask] valid_boxes = boxes[valid_score_mask] keep = nms(valid_boxes, valid_scores, nms_thr) if len(keep) > 0: cls_inds = np.ones((len(keep), 1)) * cls_ind dets = np.concatenate( [valid_boxes[keep], valid_scores[keep, None], cls_inds], 1 ) final_dets.append(dets) if len(final_dets) == 0: return None return np.concatenate(final_dets, 0)
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